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Searching in high-dimensional spaces: Index structures for improving the performance of multimedia databases
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Source ACM Computing Surveys (CSUR) archive
Volume 33 ,  Issue 3  (September 2001) table of contents
Pages: 322 - 373  
Year of Publication: 2001
ISSN:0360-0300
Authors
Christian Böhm  University of Munich, München, Germany
Stefan Berchtold  stb ag, Germany, Augsburg, Germany
Daniel A. Keim  AT&T Research Labs and University of Constance, Konstanz, Germany
Publisher
ACM  New York, NY, USA
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ABSTRACT

During the last decade, multimedia databases have become increasingly important in many application areas such as medicine, CAD, geography, and molecular biology. An important research issue in the field of multimedia databases is the content-based retrieval of similar multimedia objects such as images, text, and videos. However, in contrast to searching data in a relational database, a content-based retrieval requires the search of similar objects as a basic functionality of the database system. Most of the approaches addressing similarity search use a so-called feature transformation that transforms important properties of the multimedia objects into high-dimensional points (feature vectors). Thus, the similarity search is transformed into a search of points in the feature space that are close to a given query point in the high-dimensional feature space. Query processing in high-dimensional spaces has therefore been a very active research area over the last few years. A number of new index structures and algorithms have been proposed. It has been shown that the new index structures considerably improve the performance in querying large multimedia databases. Based on recent tutorials [Berchtold and Keim 1998], in this survey we provide an overview of the current state of the art in querying multimedia databases, describing the index structures and algorithms for an efficient query processing in high-dimensional spaces. We identify the problems of processing queries in high-dimensional space, and we provide an overview of the proposed approaches to overcome these problems.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  105

Collaborative Colleagues:
Christian Böhm: colleagues
Stefan Berchtold: colleagues
Daniel A. Keim: colleagues